Papers by Keith Harrigian
Do Models of Mental Health Based on Social Media Data Generalize? (2020.findings-emnlp)
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| Challenge: | Existing literature on the validity of proxy-based methods for annotating mental health status in social media has raised new concerns regarding their use in clinical applications. |
| Approach: | They explore the generalization ability of machine learning classifiers trained to detect depression in individuals across multiple social media platforms. |
| Outcome: | The proposed methods show that they can be used to train and analyze large datasets and that they are robust to large dataset sizes. |
Gender and Racial Fairness in Depression Research using Social Media (2021.eacl-main)
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| Challenge: | Existing studies show that social media behavior can indicate mental health of an individual . previous studies have raised concerns about possible biases in models produced from such data, but no study has investigated how these biase recur with demographic groups. |
| Approach: | They analyze the fairness of depression classifiers trained on Twitter data with respect to gender and racial/ethnic demographic groups. |
| Outcome: | The proposed model performs better for gender and racial/ethnic groups than other models and provides recommendations on how to avoid biases in future research. |
Characterization of Stigmatizing Language in Medical Records (2023.acl-short)
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Keith Harrigian, Ayah Zirikly, Brant Chee, Alya Ahmad, Anne Links, Somnath Saha, Mary Catherine Beach, Mark Dredze
| Challenge: | Widespread disparities in healthcare outcomes exist between demographic groups in the United States. |
| Approach: | They characterize disparities in medical documentation using domain-informed NLP techniques and highlight important differences between them. |
| Outcome: | The proposed methods highlight important differences between the task and bias-related tasks studied within the NLP community. |